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https://issues.apache.org/jira/browse/ARROW-17441?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17580471#comment-17580471
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Will Jones commented on ARROW-17441:
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{quote}I must admit I don't understand the references to compression in your
comments. Were you planning to use Parquet at some point?{quote}
Sorry, I was testing memory usage from Parquet reads and seeing something like
this, but decided to take Parquet out of the picture to simplify.
{quote}Other than that, Numpy-allocated memory does not use the Arrow memory
pool, so I'm not sure those stats are very indicative.{quote}
Ah I think you are likely right there.
> [Python] Memory kept after del and pool.released_unused()
> ---------------------------------------------------------
>
> Key: ARROW-17441
> URL: https://issues.apache.org/jira/browse/ARROW-17441
> Project: Apache Arrow
> Issue Type: Improvement
> Components: Python
> Affects Versions: 9.0.0
> Reporter: Will Jones
> Priority: Major
>
> I was trying reproduce another issue involving memory pools not releasing
> memory, but encountered this confusing behavior: if I create a table, then
> call {{{}del table{}}}, and then {{{}pool.release_unused(){}}}, I still see
> significant memory usage. On mimalloc in particular, I see no meaningful drop
> in memory usage on either call.
> Am I missing something? My understanding prior has been that memory will be
> held onto by a memory pool, but will be forced free by release_unused; and
> that system memory pool should release memory immediately. But neither of
> those seem true.
> {code:python}
> import os
> import psutil
> import time
> import gc
> process = psutil.Process(os.getpid())
> import numpy as np
> from uuid import uuid4
> import pyarrow as pa
> def gen_batches(n_groups=200, rows_per_group=200_000):
> for _ in range(n_groups):
> id_val = uuid4().bytes
> yield pa.table({
> "x": np.random.random(rows_per_group), # This will compress poorly
> "y": np.random.random(rows_per_group),
> "a": pa.array(list(range(rows_per_group)), type=pa.int32()), #
> This compresses with delta encoding
> "id": pa.array([id_val] * rows_per_group), # This compresses with
> RLE
> })
> def print_rss():
> print(f"RSS: {process.memory_info().rss:,} bytes")
> print(f"memory_pool={pa.default_memory_pool().backend_name}")
> print_rss()
> print("reading table")
> tab = pa.concat_tables(list(gen_batches()))
> print_rss()
> print("deleting table")
> del tab
> gc.collect()
> print_rss()
> print("releasing unused memory")
> pa.default_memory_pool().release_unused()
> print_rss()
> print("waiting 10 seconds")
> time.sleep(10)
> print_rss()
> {code}
> {code:none}
> > ARROW_DEFAULT_MEMORY_POOL=mimalloc python test_pool.py && \
> ARROW_DEFAULT_MEMORY_POOL=jemalloc python test_pool.py && \
> ARROW_DEFAULT_MEMORY_POOL=system python test_pool.py
> memory_pool=mimalloc
> RSS: 44,449,792 bytes
> reading table
> RSS: 1,819,557,888 bytes
> deleting table
> RSS: 1,819,590,656 bytes
> releasing unused memory
> RSS: 1,819,852,800 bytes
> waiting 10 seconds
> RSS: 1,819,852,800 bytes
> memory_pool=jemalloc
> RSS: 45,629,440 bytes
> reading table
> RSS: 1,668,677,632 bytes
> deleting table
> RSS: 698,400,768 bytes
> releasing unused memory
> RSS: 699,023,360 bytes
> waiting 10 seconds
> RSS: 699,023,360 bytes
> memory_pool=system
> RSS: 44,875,776 bytes
> reading table
> RSS: 1,713,569,792 bytes
> deleting table
> RSS: 540,311,552 bytes
> releasing unused memory
> RSS: 540,311,552 bytes
> waiting 10 seconds
> RSS: 540,311,552 bytes
> {code}
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